The goal of this course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding. The course starts with a recap of linear models and discussion of stochastic optimization methods that are crucial for training deep neural networks. Learners will study all popular building blocks of neural networks including fully connected layers, convolutional and recurrent layers.
Learners will use these building blocks to define complex modern architectures in TensorFlow and Keras frameworks. In the course project learner will implement deep neural network for the task of image captioning which solves the problem of giving a text description for an input image.
The prerequisites for this course are:
1) Basic knowledge of Python.
2) Basic linear algebra and probability.
Please note that this is an advanced course and we assume basic knowledge of machine learning. You should understand:
1) Linear regression: mean squared error, analytical solution.
2) Logistic regression: model, cross-entropy loss, class probability estimation.
3) Gradient descent for linear models. Derivatives of MSE and cross-entropy loss functions.
4) The problem of overfitting.
5) Regularization for linear models.
Do you have technical problems? Write to us: coursera@hse.ru

Taught By

Evgeny Sokolov

Andrei Zimovnov

Alexander Panin

Ekaterina Lobacheva

Nikita Kazeev

Transcript

[MUSIC] Hi, in this video, we will learn how to compute the gradients for MLP automatically. And for this, we need to remind you how a chain rule works, you should know that from calculus. We know how to compute derivatives for simple functions, like x squared, we have just two x. Or for exponent effects, it's just an exponent. Or for the logarithm, it's 1 over x. Let's take a composite function. That means that z1 is a function of x1 and x2, z2 is a function of x1 and x2, as well, and p is a function of z1 and z2. If we assume that z1, z2 and p are differentiable, then we can apply so-called chain rule, and we can find the derivative of p with respect to x1, dp dx1, which looks like that. So, it's dp dz1, dz1 dx1 + dp dz2, dz2 dx1. You can actually verify that this chain rule works for h(x) which is a product of functions f and g. And if you apply chain rule there, you can actually see that you got some rule that you might know from school which is a rule for finding derivative of a product. Now let's move to computational graphs. We already know that a composite function can be drawn as a computational graph. Where each node is a computed value and each edge corresponds to dependencies. Now, let's construct a similar graph, but a little bit different. Let's construct a new graph of derivatives. It will look exactly the same, with one distinction. All the edges will change their direction, and every edge will be assigned to a derivative. For example, if we have an edge coming from p to z1, that means that we will assign a value dp dz1 to that edge. And this graph actually will help us to apply chain rule, and you might guess why. Because if you look at the chain rule, it tries to compute the derivative p with respect to x1. And if you look at all the paths that it takes in this graph of derivatives, you can actually understand that a chain rule is just a sum of all those paths. So, let's verify that it actually works like that. Let's take a deeper example. Let's take an MLP where you have two hidden layers. So it is still a composite function. It is still differentiable, thanks to the choice of our errands,they're just a linear combination and differentiable activation. Let's see how we apply chain rule here. So we're trying to compute dp dx1. And to do that, we need to go through h1, right? And to computer dh1 dx1, we need to apply chain rule again because it is still a composite function, we need to go deeper. And if you apply a chain rule to those functions, you will go deeper. And if you substitute it back, you will get something like this. So you have four here that each of them is just a product of three derivatives. Let's check out the derivatives graph and let's verify that the chain rule actually has a visual explanation. So here we have our computational graph on top. We have the result that we got applying chain rule recursively, and here we got our graph in the bottom, which is a graph of derivatives. Let's verify that each sum in our derivative that we have computed actually corresponds to some path going from p to x1. Let's take the first one. And you can actually see that this is really a path from p to x1. If you go through all of them, you can actually verify that each of them corresponds to some unique path going from p to x1. And a chain rule is applied very easily. You just find that path and you multiply all the values assigned to those edges that are along that path. So the algorithm looks like this, if you want to calculate derivative of a node a with respect to other node b, all you need to do is do the following thing in loop. You find an unvisited path from a to b. You multiply all edge values in your derivative computational graph along this path and you add it to the resulting derivative. You can verify that in this simple case, as well as in more difficult case that we have reviewed previously, it actually works. And you can actually prove that it works in any case. Let's see how chain rule helps to train at least one neuron. So, let's take one neuron. It has input features x1 and x2. Then it has nodes alpha and beta, which are just parameters that we need to train. And what we do is we take linear combination of x1 and x2 with those weights. Then we calculate the sum. And then we apply a sigmoid activation function just to get the resulting z. So it's just one simple logistic regression. So how do we train it? Usually, we add some loss function to this graph which inputs not only our prediction node, z, but also a target value, y, which is a true answer for our task. So that loss function somehow tells us whether our prediction is good or not. And for SGD to work, we actually need to find the derivatives of that loss function with respect to alpha and beta. Let's see how the derivatives computational graph and chain rule helps us to do that. If you replace our graph with derivatives computational graph, that means that we actually reversed all the edges and assigned derivatives to those edges. And now we know how to find the derivative of any node with respect to any other node. So, let's try to find the derivative of L with respect to alpha. And to do that, we just need to find all the paths that go in this graph from L to alpha. And as a matter of fact, we have only one path and here it is. So we just multiply all the edge values that we have along this path, and this is our derivative. So, you can actually come up with a program that will do that for you, that will find all the paths and that will multiply that values along each path. And in the same manner, you can compute dL d beta, as well, and it is computed as easily. And of course, you should understand that you can do the same thing not for one neuron but actually for the whole MLP, because it doesn't change anything, you try to find new paths. So, let me summarize. We can use chain rule to compute derivatives of composite functions, which is MLP. We can use a computational graph of derivatives to compute them automatically because we have actually come up with an algorithm that finds all paths going from one node to the other and just multiplies all the derivatives along that path. In the next video, we will find out how to do these fast. [MUSIC]

Explore our Catalog

Join for free and get personalized recommendations, updates and offers.